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Describe features of generative AI workloads on Azure →mediumMultiple ChoiceObjective-mapped

AI-900 Practice Question: Describe features of generative AI workloads on Azure

This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

What is 'Azure AI Search' (formerly Cognitive Search) and how does it support generative AI?

A
A web crawling service that indexes publicly available web content for Azure customers
Why wrong: Web crawling/indexing is a search engine function — Azure AI Search indexes your organisation's private data for enterprise search.
B
A search service that retrieves relevant document chunks for RAG — grounding LLM responses in source material
Azure AI Search is the retrieval engine in RAG — indexing vectors and retrieving relevant chunks to ground LLM answers in real documents.
C
A service that searches Azure resource configurations for compliance violations
Why wrong: Configuration compliance is Azure Policy — Azure AI Search indexes content documents for enterprise and AI search applications.
D
A full-text search plugin that adds search to Azure SQL databases
Why wrong: SQL full-text search is a database feature — Azure AI Search is a standalone service that enables semantic and vector search across documents.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

A search service that retrieves relevant document chunks for RAG — grounding LLM responses in source material

Option B is correct because Azure AI Search is a cloud search service that indexes and retrieves relevant document chunks, which can be used in a Retrieval-Augmented Generation (RAG) pattern. By providing grounded, source-specific context to a large language model (LLM), it helps ensure the generated responses are based on factual, retrieved data rather than solely on the model's training data.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
A web crawling service that indexes publicly available web content for Azure customers
Why it's wrong here
Web crawling/indexing is a search engine function — Azure AI Search indexes your organisation's private data for enterprise search.
✓
A search service that retrieves relevant document chunks for RAG — grounding LLM responses in source material
Why this is correct
Azure AI Search is the retrieval engine in RAG — indexing vectors and retrieving relevant chunks to ground LLM answers in real documents.
Related concept
Read the scenario before looking for a memorised answer.
✗
A service that searches Azure resource configurations for compliance violations
Why it's wrong here
Configuration compliance is Azure Policy — Azure AI Search indexes content documents for enterprise and AI search applications.
✗
A full-text search plugin that adds search to Azure SQL databases
Why it's wrong here
SQL full-text search is a database feature — Azure AI Search is a standalone service that enables semantic and vector search across documents.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse Azure AI Search with a general-purpose web crawler or a simple SQL full-text search plugin, overlooking its key role as a dedicated retrieval engine for RAG in generative AI workloads.

Detailed technical explanation

How to think about this question

Under the hood, Azure AI Search uses inverted indexes and vector search (via the 2023-10-01-Preview API) to support hybrid retrieval. In a RAG workflow, the search service returns the top-k document chunks based on semantic or vector similarity, which are then injected into the LLM's prompt as grounding context. A real-world scenario is a customer support chatbot that queries a knowledge base indexed in Azure AI Search, ensuring the LLM cites the latest product documentation instead of hallucinating.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: A search service that retrieves relevant document chunks for RAG — grounding LLM responses in source material — Option B is correct because Azure AI Search is a cloud search service that indexes and retrieves relevant document chunks, which can be used in a Retrieval-Augmented Generation (RAG) pattern. By providing grounded, source-specific context to a large language model (LLM), it helps ensure the generated responses are based on factual, retrieved data rather than solely on the model's training data.

What should I do if I get this AI-900 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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